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The design of the damper is intrinsically linked to the vibrations or oscillations of the mechanical system, where achieving controllable damping is critically significant. To address challenges in tunable dampers, a damper based on annular jet was designed for tensegrity structures. Its key innovation lies in geometric control of damping performance, enabled by a theoretical model correlating geometric parameters to damping behavior through fluid flow within the gap. Subsequently, dynamic modeling and experimental verification were performed for single-degree-of-freedom (DOF) and multi-DOF systems integrated with tensegrity structures. Free vibration experiments and sequential model updating ensured accurate identification of the inherent parameters of the system. A sweep frequency vibration experiment confirmed that the dynamic behavior of the system aligns with the proposed damper design, validating both the damping effect of the annular jet and the modeling accuracy of the tensegrity structure. Next, parametric studies further analyzed the impact of design parameters on damping performance, providing configuration recommendations for damper optimization and potential applications. Furthermore, using the multi-DOF system as an example, the trajectory-based damping performance evaluation demonstrated that the current damper reduces the trajectory envelope by 16.69% compared to the system without it. Additionally, by altering the fluid medium, the proposed damper enables up up to 98.68% reduction in trajectory envelope, underscoring its customizability and significant potential for vibration suppression.

Vibration transfer analysis, a well-established method for characterizing the dynamics of mechanical systems, was integrated with a multi-degree-of-freedom (DOF) platform incorporating inertial measurement technology, to measure the dynamic behavior of complex response. A 6-DOF dynamic model was developed for a platform featuring an oblique-mounted pyramid configuration and rigidly-hinged-mixed support to address platform-based vibration analysis requirements. Vehicle excitation served as the vibration source to measure the dynamic response of the platform, which was converted into a transfer function to quantify vibration transfer while reducing noise interference. A transfer function-oriented model updating method improved accuracy, and the parametric study using the updated model guided the vibration isolation optimization. Results demonstrate that the proposed platform configuration achieves effective 6-DOF vibration isolation performance in the low-frequency range.

This study explores the complex relationship between wood moisture content and environmental factors, temperature and relative humidity. Utilizing a novel Autoregressive Polynomial Regression Model (APRM), data from sensors placed in reconstituted bamboo and pine planks at various positions were analyzed. The APRM, adept at handling polynomial and interaction terms, revealed a nuanced, non-linear relationship between moisture content and environmental conditions. The research findings underscore significant material-specific differences in response to environmental changes. This study not only contributes to the understanding of wood-environment interactions but also demonstrates the efficacy of APRM in environmental science, providing a foundational approach for future research in this field. © 2024 IEEE.

Building ventilation system needs to be controlled in a smart way to maintain indoor air quality while reducing energy use. Although many demand-controlled methods have been developed, the design of ventilation schedule has to be customized depending on local climate, occupant behavior and system capacity. This article introduces an easy-to-use control strategy based on mathematical modeling, clustering and genetic algorithm. Experimental results improve the performance of current system in an example house and provide a data-driven framework. © 2024 Elsevier Inc. All rights are reserved.

To maintain the life of building materials, it is critical to understand the hygrothermal transfer mechanisms (HTM) between the walls and the layers inside the walls. Due to the extreme instability of weather data, the actual data models of the HTM—the data being collected for actual buildings using modern sensor technologies—would appear to be a great difference from any theoretical models, in particular, for wood building materials. In this paper, we aim to consider a variety of data analysis tools for hygrothermal transfer features. A novel approach for peak and valley detection is proposed based on the discrete differentiation of the original data. Not to be limited to the measure of peak and valley delays for HTM, we propose a cross-correlation analysis to obtain the general delay between two daily time series, which seems to be representative of the delay in the daily time series. Furthermore, the seasonal pattern of the hygrothermal transfer combined with the correlation analysis reveals a reasonable relationship between the delays and the indoor and outdoor climates. © 2023 by the authors.

This review aims to comprehensively assess and synthesize the existing literature on the use of data-driven methods for studying hygrothermal transfer in building exterior walls. The review is conducted by an exhaustive search strategy to identify relevant articles from Web of Science and Scopus databases. There are 20 eligible studies included in this review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The most used data-driven methods are traditional neural networks, such as Multi-Layer Perceptrons and 2D Convolutional Neural Networks. Results suggested that neural network models hold potential for accurately predicting hygrothermal attributes of building exteriors. However, a conspicuous gap in the literature is the absence of studies drawing direct comparisons between data-driven methodologies and conventional simulation techniques. © 2023 ACM.